Principal Investigator: Dr Eugene Melamud

1a: It is well understood that burden of disease increases with age, however it remains unknown why some individuals are able to reach advanced ages without experiencing major physical and mental disability, while majority will succumb to plethora of diseases including dementia, cardiovascular diseases, and cancer. It is believed that healthy longevity phenotype arises from complex interaction between individuals’ genotype and environmental variables, but due to lack of adequately powered studies, thus far predictive models of healthspan have not been developed. Large scale health status description of population by UK Biobank represents a unique opportunity to build a comprehensive model of physiological decline and could potentially lead to identification of key factors that determine healthy lifespan.

1b: Disabilities associated with aging are expected to become a dominant burden on public health systems, especially so in the OECD countries where increasing fraction of population is surviving to advanced ages. We are hoping that insights gained from this study will improve well-being of elderly population and may lead to interventions that increase lifespan while at the same time reducing overall morbidity in population.

1c: We will use UK Biobank data to derive parameters associated with age related physiological decline. A variety of machine learning algorithm will be used to identify genetic and environmental factors that are predictive of rate of individuals’ decline, overall morbidity, and mortality. In order to build comprehensive model, data from UK Biobank will be supplemented with data from other sources, such as genetic predispositions defined in prior GWAS studies and known disease comorbidity associations.

1d: We are asking for access to complete cohort across all age groups with genetic and physiological measurement data. In a long term, we would like to follow up on subset of individual for more comprehensive longitudinal physiological assessments.